GEFCOM 2014—Probabilistic Electricity Price Forecasting

Barta, Gergo; Borbély, Gyula; Nagy, Gábor P.; Kazi, Sandor; Henk, Tamas

doi:10.1007/978-3-319-19857-6_7

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…To justify the usage of a new approach in time series forecasting it is often benchmarked with an established methodology like ARMA on a set of time series in the respective domain [2]. In this particular application, if provided in the open access data, the country issued estimates of energy consumption are used to assess the relative accuracy of the GBRT methodology.…”

Section: A Model Evaluation and Results Comparisonmentioning

confidence: 99%

Forecasting framework for open access time series in energy

Barta

Nagy

Papp

et al. 2016

2016 IEEE International Energy Conference (ENERGYCON)

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In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solution was benchmarked using the actual load data and the country provided estimates (where available). We conclude that the proposed system can produce timely forecasts with comparable prediction accuracy in a number of cases. We also investigate the probabilistic case of forecasting -that is, providing a probability distribution rather than a simple point forecast -and incorporate it into a web based API that provides quick and easy access to reliable forecasts.

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Section: A Model Evaluation and Results Comparisonmentioning

confidence: 99%

Forecasting framework for open access time series in energy

Barta

Nagy

Papp

et al. 2016

2016 IEEE International Energy Conference (ENERGYCON)

Self Cite

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“…To obtain numerical results, we utilize energy data from global energy forecasting competition 2014 (GEFCom2014) in [35]. For the construction of the prosumers, we generate hourly energy change data by combining hourly solar generation data and hourly energy consumption data as in Table 2, Table 3, and Table 4.…”

Section: A Data Setmentioning

confidence: 99%

Time Series Forecasting Based Day-Ahead Energy Trading in Microgrids: Mathematical Analysis and Simulation

2020

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In this paper, we propose a periodic energy trading system in microgrids based on day-ahead forecasting of energy generation and consumption. In the proposed model, each noncooperative prosumer calculates her reward function under her energy change forecasting based on Gaussian process regression and determines her optimal action. Then, the system establishes the equilibrium trading price when all prosumers execute their optimal actions simultaneously. We prove the existence of the equilibrium trading price and establish an algorithm that leads to the equilibrium. Our numerical example shows that the proposed system outperforms its previous model. INDEX TERMSMicrogrids, smart grids, energy trading system, future forecasting, Gaussian process regression.

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“…From the analysis above, we can find out that most enterprises are highly correlated with time features, so we can extract temporal features [48] as attributes for feature construction. Sahay et al [49] introduced the influence of temperature to electricity consumption, so we also consider the effect of temperature.…”

Section: (2) Model Prejudgement Based On Periodicity/trend and Nonlinmentioning

confidence: 99%

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

Zheng

Yang

et al. 2018

Wireless Communications and Mobile Computing

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With the rapid development of IoT, the disadvantages of Cloud framework have been exposed, such as high latency, network congestion, and low reliability. Therefore, the Fog Computing framework has emerged, with an extended Fog Layer between the Cloud and terminals. In order to address the real-time prediction on electricity demand, we propose an approach based on XGBoost and ARMA in Fog Computing environment. By taking the advantages of Fog Computing framework, we first propose a prototypebased clustering algorithm to divide enterprise users into several categories based on their total electricity consumption; we then propose a model selection approach by analyzing users' historical records of electricity consumption and identifying the most important features. Generally speaking, if the historical records pass the test of stationarity and white noise, ARMA is used to model the user's electricity consumption in time sequence; otherwise, if the historical records do not pass the test, and some discrete features are the most important, such as weather and whether it is weekend, XGBoost will be used. The experiment results show that our proposed approach by combining the advantage of ARMA and XGBoost is more accurate than the classical models.

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GEFCOM 2014—Probabilistic Electricity Price Forecasting

Cited by 13 publications

References 13 publications

Forecasting framework for open access time series in energy

Forecasting framework for open access time series in energy

Time Series Forecasting Based Day-Ahead Energy Trading in Microgrids: Mathematical Analysis and Simulation

Predicting Short‐Term Electricity Demand by Combining the Advantages of ARMA and XGBoost in Fog Computing Environment

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